TY - JOUR
T1 - A little bird told me your gender
T2 - Gender inferences in social media
AU - Fosch-Villaronga, E.
AU - Poulsen, A.
AU - Søraa, R. A.
AU - Custers, B. H.M.
N1 - Publisher Copyright:
© 2021 The Author(s)
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/5
Y1 - 2021/5
N2 - Online and social media platforms employ automated recognition methods to presume user preferences, sensitive attributes such as race, gender, sexual orientation, and opinions. These opaque methods can predict behaviors for marketing purposes and influence behavior for profit, serving attention economics but also reinforcing existing biases such as gender stereotyping. Although two international human rights treaties include explicit obligations relating to harmful and wrongful stereotyping, these stereotypes persist online and offline. By identifying how inferential analytics may reinforce gender stereotyping and affect marginalized communities, opportunities for addressing these concerns and thereby increasing privacy, diversity, and inclusion online can be explored. This is important because misgendering reinforces gender stereotypes, accentuates gender binarism, undermines privacy and autonomy, and may cause feelings of rejection, impacting people's self-esteem, confidence, and authenticity. In turn, this may increase social stigmatization. This study brings into view concerns of discrimination and exacerbation of existing biases that online platforms continue to replicate and that literature starts to highlight. The implications of misgendering on Twitter are investigated to illustrate the impact of algorithmic bias on inadvertent privacy violations and reinforcement of social prejudices of gender through a multidisciplinary perspective, including legal, computer science, and critical feminist media-studies viewpoints. An online pilot survey was conducted to better understand how accurate Twitter's gender inferences of its users’ gender identities are. This served as a basis for exploring the implications of this social media practice.
AB - Online and social media platforms employ automated recognition methods to presume user preferences, sensitive attributes such as race, gender, sexual orientation, and opinions. These opaque methods can predict behaviors for marketing purposes and influence behavior for profit, serving attention economics but also reinforcing existing biases such as gender stereotyping. Although two international human rights treaties include explicit obligations relating to harmful and wrongful stereotyping, these stereotypes persist online and offline. By identifying how inferential analytics may reinforce gender stereotyping and affect marginalized communities, opportunities for addressing these concerns and thereby increasing privacy, diversity, and inclusion online can be explored. This is important because misgendering reinforces gender stereotypes, accentuates gender binarism, undermines privacy and autonomy, and may cause feelings of rejection, impacting people's self-esteem, confidence, and authenticity. In turn, this may increase social stigmatization. This study brings into view concerns of discrimination and exacerbation of existing biases that online platforms continue to replicate and that literature starts to highlight. The implications of misgendering on Twitter are investigated to illustrate the impact of algorithmic bias on inadvertent privacy violations and reinforcement of social prejudices of gender through a multidisciplinary perspective, including legal, computer science, and critical feminist media-studies viewpoints. An online pilot survey was conducted to better understand how accurate Twitter's gender inferences of its users’ gender identities are. This served as a basis for exploring the implications of this social media practice.
KW - Algorithmic bias
KW - Automated gender recognition system
KW - Discrimination
KW - Gender
KW - Gender classifier
KW - Gender stereotyping
KW - Inference
KW - LGBTQAI+
KW - Online Behavioral Advertising
KW - Privacy
KW - Social media
KW - Twitter
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U2 - 10.1016/j.ipm.2021.102541
DO - 10.1016/j.ipm.2021.102541
M3 - Article
AN - SCOPUS:85101396748
SN - 0306-4573
VL - 58
JO - Information Processing and Management
JF - Information Processing and Management
IS - 3
M1 - 102541
ER -